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Home/ Questions/Why does the valueerror: cannot index with vector containing na / nan values error occur?
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Cecil O'Kelly
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Cecil O'Kelly
Asked: May 18, 20222022-05-18T20:33:52+00:00 2022-05-18T20:33:52+00:00In: python

Why does the valueerror: cannot index with vector containing na / nan values error occur?

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I am tired of fixing the problem: valueerror: cannot index with vector containing na / nan values in the python; even if I get the reference from another forum, it still returns an error:

ValueError: cannot index with vector containing NA / NaN values

To identify the problem, I will show you the detail here:

import csv
 import os
 import pandas as pd
 os.chdir('C:\\Users\\khalha\\Desktop\\RealExcel')
 filename = 'sales.csv'
 
 Sales = pd.read_csv('sales.csv')
 iFlowStatus = Sales[Sales['Product'].str.contains('iFlow')]['Status']
 print(iFlowStatus)

How do I do that? Could you support me in improving this problem?

cannot index with vector containing na/nan values
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    lyytutoria Expert
    2022-06-27T09:38:24+00:00Added an answer on June 27, 2022 at 9:38 am

    The cause:

    You have got ”cannot index with vector containing na / nan values” error because your dataframe includes empty entries which always set default to NA or NaN.

    Solution:

    You can solve this error simply by adding na=False in the synatx to fill the missing or lost values as the following:

    import csv
    import os
    import pandas as pd
    os.chdir('C:\\Users\\khalha\\Desktop\\RealExcel')
    filename = 'sales.csv'
    
    Sales = pd.read_csv('sales.csv')
    iFlowStatus = Sales[Sales['Product'].str.contains('iFlow', na=False)]['Status']
    print(iFlowStatus)
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  2. Lucas Besson
    2022-05-25T21:06:32+00:00Added an answer on May 25, 2022 at 9:06 pm

    Another issue is that mixed-type columns can cause this problem if they do not contain NaN. Take this example:

    > df = pd.DataFrame({'x': ['hi', 99]})
    > df.x.isna().any()
    False
    > df[df.x.str.contains('hi')]
    ...
    ValueError: cannot index with vector containing NA / NaN values

    Depending on your intent, you have the option to cast (df.x.astype(str).str.contains('hi')), or drop the row that is not working.

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